MANDERA WEST SUB COUNTY, KENYA. 6 th to 17 th October 2013 Caroline Njeri KIMERE

Transcription

1 MANDERA WEST SUB COUNTY, KENYA 6 th to 17 th October 2013 Caroline Njeri KIMERE

2 ACKNOWLEDGEMENTS Special thanks are expressed to; United Nations Children s Fund (UNICEF) for the continued financial support to Save the Children Nutrition program and for funding this survey. Save the Children National office and the teams in Mandera West and Banisa (Health, nutrition and Monitoring and evaluation) especially the nutrition officer Josephat Ogeto for his technical and moral support. The Ministry of Health (MoH) for their support and commitment and all the health workers who participated in the survey The Survey team (enumerators and drivers) for their tireless efforts to ensure that the survey was conducted professionally and on time. Community members who willingly participated in the survey and provided the information needed. This study would not have been possible without the hard work and commitment of everyone involved.

3 EXECUTIVE SUMMARY Mandera West and Banisa are in the greater Mandera County at the extreme end of the North Eastern Kenya and bordering Ethiopia to the North. With a combined estimated population of approximately 319,775 people according to the 2009 census and Km 2, Mandera West and Banisa form a vast and arid region. Climatic hazards such as drought and floods have had a devastating impact on the traditional livelihoods of pastoralist resulting in increased sedentarization and diversification to alternative livelihood activities. Insecurity emanating from clan conflict has resulted in displacement of populations hence deprivation of livelihood resulting in increased incidence of malnutrition and negative coping mechanism e.g. charcoal burning.. The rates of global acute malnutrition (GAM) have remained persistently above the emergency threshold of 15%. Findings of the SMART survey conducted in 2013 reveal that t16.2% 1 of children under five are acutely malnourished. Further to this, complementary feeding indicators remain low across Mandera West and Banisa. These indicators may further plummet as a result of the current situation described earlier. To prevent further deterioration in acute malnutrition and consolidate gains from previous programming Save the Children through funding from UNICEF has been supporting the scaling up of High Impact Nutrition Interventions (HINI) and strengthening health service delivery through secondment of nurses to the health facilities and staff capacity building to boost quality service delivery, through a period of 14 month from December 2012 to February Resume of coverage assessment The coverage assessment was conducted to evaluate access and coverage of the Community based Management of Acute Malnutrition programme for children ages 6 to 59 months with SAM. It conducted between the 6 th to the 17 of October 2013 and it was the second Coverage survey to be conducted in Mandera West. It was conducted at the beginning of the Haggai dry season. Year 2012 (December) 2013(December) Assessment SQUEAC SQUEAC Coverage results (point coverage) Outreach sites 56.1% (41.1% % CI) Health Facility 40.4% (28.6% % CI) 46.2% (33.6%-59.7% 95 CI) The SQUEAC methodology used consisted of 3 stages, applying the principles of triangulation (by source and method) and sampling to redundancy. The coverage investigation conducted in Mandera West district: point coverage is 46.2% (95% IC: %) The table below presents the main barriers on which the program must act to improve coverage as well as specific recommendations how to do so: 1 Save the Children SMART survey April 2013

4 Barriers Lack of awareness of malnutrition by community members and some caretakers Inadequate mobilization and poor active case finding by the community health workers Too little time spent with beneficiaries by the OTP nurses and CHWs during distribution days Migration and nomadic nature of some part of the population. Poor human resources (too few nurses and CHWs and frequent turn over) Boosters Availability of free services for under-fives at the health facilities and integration of health and nutrition during outreaches Good treatment outcomes ( SPHERE) Referral of Severe Acute Malnutrition (SAM) cases through provision of ambulance services to and from the Stabilization centre Availability of outreach services in the hard to reach areas Appreciation of the program by caretakers and community members Recommendations Holding community dialogue sessions in all facility and outreach sites for the community members but as well as targeted to men Work at making the program to be responsive to the needs of the nomadic populations Advocate for employment of health and nutrition focal persons in Sub-County level Development of work plans /schedules of active case finding Strengthen use of CHW reporting tools Planning and commitment on outreach implementation Continuous RUTF monitoring, and replenishing stock from the District Hospital with the cars going for outreach in all facilities weekly Advocate for CHWs incentives to march with work loads

5 CONTENTS 1. INTRODUCTION CONTEXT Results of previous SQUEACs in Mandera West OBJECTIVES METHODOLOGY GENERAL OVERVIEW STAGES Stage 1: Identification of potential areas of high and low coverage and access barriers Stage 2: Small area surveys Stage 3: Building the prior and conducting wide area survey to estimate overall coverage ORGANIZATION OF THE EVALUATION LIMITATIONS RESULTS STAGE STAGE STAGE The prior The likelihood The posterior DISCUSION RECOMMENDATIONS Annex 1 : Survey questionnaire for current SAM children NOT in the program Annex 2: Mandera West SQUEAC plan, September October Annex 3 : SQUEAC Survey team Annex 4 : Terminology in Somali (S) and Borana (B) used to describe malnutrition and RUTF Annex 5: Weighted BBQ, Mandera West SQUEAC, October

6 ABBREVIATIONS AFREN BBQ CI CMAM CMN COCOP ECHO FGD GAM HC HF IMAM INGO IRK LoS MAM MEAL MoH MUAC OCHA OS OTP RUTF SAM SC SCI SFP SQUEAC TBA UNDP UNICEF VSF WHO Barriers, Boosters and Questions Credible Interval Community Management of Acute malnutrition Coverage Monitoring Network Consortium of Cooperating Partners European Commission - Humanitarian Aid & Civil Protection Focus Group Discussion Global Acute Malnutrition Health Centers Health Facility Integrated Management of Acute Malnutrition International Non-Governmental Organisation Islamic Relief Kenya Length of Stay Moderate Acute Malnutrition Monitoring, Evaluation, Accountability and Learning Ministry of Health Mid-Upper Arm Circumference Office for the Coordination of Humanitarian Affairs Outreach Site Outpatient Therapeutic Programme Ready to Use Therapeutic Food Severe Acute Malnutrition Stabilization Centre Save the Children International Supplementary Feeding Program Semi Quantitative Evaluation of Access and Coverage Traditional Birth Attendants United Nation Development Programme United Nations Children s Fund Vétérinaires Sans Frontières World Health Organisation

7 1. INTRODUCTION 1.1 CONTEXT Overview of the area The Larger Mandera West is part of the greater Mandera County located at the extreme end of North Eastern Kenya bordering Ethiopia to the North. The Larger Mandera West with an estimated population of 132,000 is comprised of 2 Sub-Counties namely, Mandera West and Banisa. The two Sub- Counties have five Divisions namely Takaba and Dandu (Mandera West Sub-County) and Kiliwehiri, Malkamari and Banisa (Banisa Sub-County). The population is of Somali ethnicity and is divided into two clans (Garre and Degodia. This is an Agro -pastoral zone with livestock herding serving as the main source of livelihoods. Shoats and camels are the most common livestock species kept with minority of households owning some cattle. Mandera West and Banisa form part of the ASAL (Arid and Semi-Arid Lands) Districts of Kenya. Most of its rural areas depict poor level of infrastructure development. The communities living in the region have continuously suffered food insecurity for the past 10 years as a result of extreme climates characterized by a succession of drought and floods. This has led to devastating impact on the traditional livelihoods of pastoralist populations in the area as a result of low milk production and loss of livestock. There are two rainy seasons in the area, Long rains (April-June), and the short rains (October November). A few people plant sorghum and maize, and vegetables like tomatoes cabbages, and Kale during the rains but majority depend on rain for water and pastures for their animals. During the dry spells community move to neighbouring Ethiopia in search of water and pasture Figure 1: Map Mandera West Sub-County (highlighted in red), Kenya 2. 2 From wikipedia : (visited on January 2014)

8 Nutritional situation Regarding the nutritional situation, Save the Children International (SCI) in conjunction with the Ministry of Health (MoH) and the District steering Group has been conducting annual nutrition SMART surveys (before the long rains) to monitor the Nutrition situation. Global acute malnutrition (GAM) rates have surpassed the WHO alert threshold for a state of emergency in all the Survey results of the larger Mandera West for the past 7 years. The highest GAM was in 2011 April 32.6 % that led to Blanket supplementary Feeding intervention in the entire Mandera County and other ASL fr om August to March In the last survey conducted in April 2013 result showed that GAM was still critical above the emergency threshold according to WHO classification 2. The GAM rate was 16.2% whereas Severe Acute Malnutrition (SAM) rate was 3.3%. Looking only at MUAC criteria in the 2013 April nutrition survey, GAM rate was at 5.7 % and SAM at 1.7%. Figure 2: Results of nutrition surveys in Mandera West Sub-County, KENYA In Country, malnutrition rates have been chronically at emergency levels. These high rates of malnutrition can be attributed to poor health conditions, sub-optimal maternal and child feeding, care practices, and food insecurity. This has been compounded by high rates of poverty and illiterac y, marginalization, recurrent environmental shocks (floods and droughts) and displaced populations adding an additional strain to already weak health systems and communities 3. In Mandera County, malnutrition rates are chronically at emergency levels. The county continues to have high rates of malnutrition attributed to poor health conditions, sub-optimal maternal and child feeding, care practices, and food insecurity. The prolonged and recurring drought situation has persistently weakened the community s resiliency and coping mechanisms due to short recovery phase between droughts. Poor infrastructure including inaccessible roads, inadequate health system and staff shortage have continuously affected nutrition service delivery. 3 SMART survey report 2013, Kenya Demographic Health Survey 2008

9 Health access in Larger Mandera West District Health services are delivered both at outreach Sites (hard to reach areas and at health centres. Save the Children with a funding from European Commission (EC) and Afren plc is supporting the Ministry of Health to provide essential basic health care at the health centres and Outreach in hard to reach areas. Save the Children also supported the establishment of two community units in Dandu and Banisa Locations with an aim to improve health care access and awareness. There are Community Health Volunteers (CHV) attached to every facility and major settlements responsible for screening and mobilizing children under 5 and pregnant and lactating women. They can detect cases of some diseases (including malnutrition) and refer them to the health facilities and for outreach service in hard to reach areas. The two Community units in Dandu and Banisa have the highest CHV work force of 40 CHVs each providing basic services like screening for malnutrition and referral sick patients especially pregnant lactating and children under five years. The CHV are attached to Community Health Extension Workers (CHEWs) who guide and supervise their work. The CHEWs are salaried government employees hence the role played by them is essential for sustainable program each CHEW is attached to 20 CHWS. The CHWs in the community unit report to the CHEW who will in turn compile a monthly report for the catchment facility with the help of the health facility in charge. Nutrition services In the lager Mandera West, nutrition Outpatient Therapeutic Program (OTP) services are delivered by the MoH with support from Save the Children International (UNICEF grant). From September to March February 2013 Nutrition services were only provided in 8 health facilities (HF) following funding Gap. Beginning March 2013 Save the Children commenced support to the MoH purposely to strengthen the capacity of Mandera West District Health Service (MoH) to effectively deliver and manage high impact nutrition interventions at facility and community levels. This led to increased awareness and access to integrated Health and Nutrition services in 43 hard to reach sites. In addition to establishment of outreach program the Ministry was support with 14 nurses who were deployed to rural facility which did not have trained health workers including Malkamari, Eymole, Gither, Burduras, Mobile Clinic and Derkale which became operational for the first time in March Some of the nurses were also deployed to the district hospitals Banisa and Takaba which had very few staff who could not manage the heavy workload. In the previous grant which ended in June 2013, Save the Children was directly implementing the outreach program with a full paid outreach team including a nurse, Nutritionist, Health and hygiene promotion officer and mobilisers. The current grant saw a change in the implementation strategy whereby MoH staff were fully in charge of all facility based and outreach Nutrition services with logistic and capacity support from save the children tech nical staff. In the larger Mandera west the admission criteria is based on national protocol for SAM management, at Health facility and Outreach level the activity implemented by health workers from catchment health facilities, admission criteria is MUAC <115mm (with length >65cm) and/or bilateral pitting oedema Weigh for Height <-3 Z-score using the WHO-2006 standards), MUAC < 115 mm with (with length > 65 cm) and/ or presence of bilateral pitting oedema. UNICEF provides the Ready to Use Therapeutic Food (RUTF) and medicines for SAM treatment.

10 For Moderate Acute Malnutrition Management (MAM), Supplementary food is provided by the World Food Programme (WFP) through their Lead Agency COCOP. Beneficiaries for both programs are identified through regular mass screening at every three months. Therapeutic and Supplementary feed pipeline has improved significantly with support from Save the Children who have ensured requests are submitted on time to the commodity providers (UNICEF and WFP) Save the Children in district Save the children has been in Mandera West Sub-county since 2009 with an ECHO program cofunded with Vétérinaires Sans Frontières (VSF) Swiss. The role of Save the Children was to provide health education and cooking demonstrations to the food voucher beneficiaries. The program thereafter evolved through funding from OFDA to a capacity building program where MOH staffs were trained on key components to be able to implement an IMAM program. Form this training the MOH was provided for support (Human resources, logistics and supplies) by UNICEF in 2011 and have ben implementing IMAM in static health facilities and outreach sites.

11 1.2 Results of previous SQUEACs in Mandera West. In order to assess and improve program performance in terms of access and coverage, one assessment was carried out in Mandera West in August September 2012 using SQUEAC methodology. Table 1 show a summary of the main results of this assessment. Table 1: Results of coverage assessments, , Mandera West district, North Eastern Province in Kenya. Year Assessment Zone / OTP sites MUAC admission Coverage results 4 (point) 2012 (August-September) SQUEAC Mandera West district 38 Outreach Sites and supporting the MoH in 10HF (total 48 OTP sites) < 115 mm 1) Health Facilities (MOH): 56.1% (95% CI: 41.1% %) 2) Outreach (Save the children) : 40.4% (95% CI : 28.6% %) Main barriers - Migration - Insecurity - Children in SFP - Poor infrastructure/lack of access to beneficiaries during the rainy season - Ignorance/child neglect Specific to MoH - Lack of appreciation of the program - Program rejection - Lack of Community mobilisation - Long waiting time in program 4 Coverage: Health Facilities (MOH): 48.3% (95% IC: ) Outreach (Save the children) : 67.6% (95% IC: )

12 2. OBJECTIVES Main objective The main objective of this assessment was to evaluate access and coverage of the Integrated Management of Acute Malnutrition (IMAM) for children ages 6 to 59 months with SAM in Mandera West district, North Eastern Province in Kenya, using the Semi-quantitative evaluation of access and coverage (SQUEAC) methodology. Specific objectives - To develop capacity of various stakeholders on undertaking program coverage assessments using SQUEAC methodology - To determine baseline coverage for IMAM - To identify boosters and barriers influencing IMAM program access and coverage - To develop feasible recommendations to improve IMAM program access and coverage - To compare and monitor progress since the previous SQUEAC conducted in Wajir South

13 3. METHODOLOGY GENERAL OVERVIEW The Semi-Quantitative Evaluation of Access and Coverage (SQUEAC) is a coverage assessment method developed by Valid International, FHI 360/FANTA, UNICEF, Concern Worldwide, World Vision International, Action Against Hunger, Tufts University, and Brixton Health. The methodology is semi-qualitative in nature, meaning that it draws from a mixture of both quantitative data from routine program monitoring activities as well as qualitative data collected on the field. This mixed methods approach combines data sources to estimate program coverage and to develop practical measures that can improve access and coverage. - Quantitative data came mainly from routine monitoring information that the program already collected including: admissions, defaulting, recovery, middle upper arm circumference (MUAC). Routine program data was coupled with complementary data like agriculture, labor, and disease calendars, anthropometric nutritional surveys, and agricultural and food security assessments. - Qualitative data collected came from interviews, focus groups and questionnaires with various key informants. Together, the data were triangulated by source and method to formulate hypotheses about coverage and access. Data triangulation is a powerful technique that helped validate our findings through cross verification. Hypotheses were then tested with small-area surveys and small sample surveys. Then, a wide area survey was conducted in the community to determine the point coverage estimate. Lastly, the results from the quantitative and qualitative analyses and the wide-area likelihood survey were combined and the overall global coverage estimate was calculated using Bayesian statistical techniques. The coverage assessment was fourth coverage study in the area. It was conducted between the 6 th to the 17 of October It was carried out at the end of the dry season and when food availability was apparently good. The SQUEAC methodology used consisted of 3 stages, applying the principles of triangulation (by source and method) and sampling to redundancy STAGES SQUEAC and SLEAC Technical Reference. FANTA. Available at UEAC -SLEAC-Technical-Reference-Oct2012_0.pdf 13

14 Stage 1: Identification of potential areas of high and low coverage and access barriers Identification of potential areas of high and low coverage using routine program data; in this stage, triangulation of data is going to be done by various sources and methods as highlig hted below. 1. Recommendations follow up of SQUEAC August-September 2012 Analysis of recommendations from the SQUEAC of August-September 2012 follows up. The evolution of the factors influencing coverage positively and negatively was studied. 2. Quantitative data Quantitative, routine program data helped to evaluate the general quality of IMAM services, to identify admission and performance trends and to determine if the program adequately responds to need. It also helped point out problems in screening and admission. Lastly, routine program data analysis provided the first insights into variation in program performance between OTPs. Route program data analysis (January 2011 August 2013) 48 OTP: 38 OS + 10 HF - Global (OTP and SC) trends of admission and defaulters over time and compared to the agricultural calendar, the lean period, child epidemics and diseases, workload, weather patterns and key events - Admission: admission by OTP and SC - OTP and SC program performance indicators over time (recovery, default, death, nonresponse). - Stock break out data. Complementary from children card (May September 2013) for 33 from 48 OTP: 38 OS + 10 HF - MUAC at the time of admission - Discharged o Cured: length of stay (LoS) and MUAC at discharge. o Defaulters: length of stay (LoS) and MUAC at discharge. - OTP admissions by category (MUAC, W/H and Oedema) - The village lists populations belonging to each OTP and distance walking to OTP. Admissions per village Not available - Admissions and defaulters per village - Source of referral to the OTP 3. Qualitative data 14

15 Qualitative data was collected to investigate program operations, to unravel the opinions and experiences of actors involved in IMAM and to identify any potential barriers to access. The following methods were used: focus groups, semi-structured interviews, structured interviews, case studies, observation and information from previous coverage assessments. Interviews and focus groups were conducted with key informants either directly or indirectly involved in the IMAM program. These included: women s and men s community, SCI program staff, local authorities, OTP/SC nurses, CHW, caregivers of SAM children, Informal caregivers (traditional healers and traditional birth attendants), partners (WFP, COCOP) and mother to mother support group and county HDA. Finally we couldn t meet county or sub county health authorities. The BBQ framework. Throughout the investigation, the data are going to be organized, analyzed and triangulated using the Barriers, Boosters and Questions (BBQ 6 ) framework. It is a tool that facilitates iterative data collection that is then categorized into one of three categories. The various data organized within the BBQ framework, when combined, will help providing information about where coverage is likely to be satisfactory as well as where it is likely to be unsatisfactory. Additionally, the BBQ provided information about likely barriers to services access that exists within the IMAM program. Stage 2: Small area surveys The goal of stage 2 is to test the hypotheses about coverage and access elaborated in stage 1. These hypotheses usually take the form of identifying areas where the combined data suggest that coverage is likely to be either high or low. The small-area surveys method was used to test the hypotheses for IMAM high and low coverage areas. The active and adaptive case-finding methodology was used to find SAM cases. Data surveys will be analysed using simplified lot quality assurance sampling (LQAS). The LQAS classification technique analyses data using the following formula: where If the number of covered cases found (that is, those cases in the program) is greater than then then the coverage of the surveyed area is classified as being greater than or equal to the coverage standard. If the number of covered cases found (that is, those cases in the program) is less than then then the coverage of the surveyed area is classified as being less than or equal to the coverage standard 6 Barriers are negative findings that deter from program coverage and complicate access to service. Conversely, boosters contribute to a higher coverage and facilitate access. Lastly, questions, are those findings elements to be further investigated, and either become a barrier or booster or remain inconclusive 15

16 The threshold chosen was 50.0%. The recommended coverage for rural populations (SPHERE). If the number of covered cases found (that is, those cases in the program) is less than then then the coverage of the surveyed area is classified as being less than or equal to the coverage standard Stage 3: Building the prior and conducting wide area survey to estimate overall coverage. The goal of stage three is to calculate the overall coverage estimate. This is done using a Bayesian statistical technique called beta-binomial conjugate analysis. Conjugate analysis begins with a beta distributed, probability density called the prior. The prior is then combined with a binomial distributed, likelihood function called the likelihood. The likelihood is going to be determined by a wide-area coverage survey that will be conducted across the entire program catchment area; the mode of the likelihood was, in fact, the point coverage estimate from the survey. Because the prior and the likelihood are mathematically expressed in similar ways (as probability distributions) they can be combined through conjugate analysis, the result of which is the po sterior probability density the posterior. The mode of the posterior is the final coverage estimate. 1. The Prior The prior was constructed by combining the results from stages 1 and 2, that is: routine program data, qualitative data and all relevant findings from the small-area and small sample surveys. The prior was the result of combining four modes: 1) The Simple BBQ. The simple BBQ is the first and simplest approach to calculating the prior. A uniform score of 5 points was attributed to each element (either a barrier or booster). The total booster and total barrier scores were summed. The total booster score was then added to the minimum possible coverage (0%) and the total barrier score was subtracted from the maximal possible coverage (100%). The coverage estimate was calculated by taking the mean of these two percentages. 2) The weighted BBQ : a score from 1 to 5 was attributed to each element. The score reflected the relative importance or likely effect that the element had on coverage. The coverage estimate was calculated by the method explained above. 3) The concept map : is a graphical analysis technique that was used to organize the data. The final product, the concept-map, is a diagram that visualizes relationships between findings. It was elaborated within a context frame, which is defined by an explicit focus topic. Links were drawn between each concept, representing the relationship between them. The various relationships types traced included: results in, leads to, encourages, helps create, allows, etc. Two concept maps were created, for barriers and boosters. For each map, the total number of linkages was counted. Like before the booster linkage sum was added to the minimum possible coverage value (0%) while the barrier linkage sum was subtracted fr om to the maximum possible coverage value (100%). The coverage estimate was calculated by taking the mean of these two percentages. 16

17 4) The histogram prior: During a participatory working group, the investigation team designed a histogram representing the prior mode. This was done realistically and democratically. The mode, minimum and maximum coverage values were chosen credibly. 2. The likelihood A wide-area likelihood survey was conducted over the entire program catchment area to calculate the coverage estimate. The active and adaptive case-finding methodology was used to identify the SAM cases. The case definition used for coverage survey was defined as a child matching the admission criteria of the programme. The admission criteria of the Kenyan IMAM programme included children age between 6 and 59 months with at least one of the following criteria: 1) a MUAC of <115 mm and/or 2) W/H < - 3 Z-scores and / or 3) bilateral pitting oedema A simple structured interview questionnaire was used to caregivers of non-covered cases for SAM Annex 1. The sample size required was calculated by using the following equation: 1. Mode: prior value expressed as a proportion. 2. α et β: shape parameters of the prior. 3. Precision : desired precision. In the present case the precision used was (13.5%). 4. SAM prevalence: 0.2% was chosen after stage 2 results. Initially the rates considered were 0.7%, the prevalence in the last SMART survey in May 2013 (for MUAC admission criteria) in Mandera West district. But the prevalence was found inferior to these data and this was revised downwards following the results from stage Average village population: 1,817 population in Mandera West (based on district health office data which is projected from the 1999 census since the 2009 data was refuted) 6. Population between 6 and 59 months : approximately 20.0% And the sample size will was into the minimum number of villages needing to be sampled to achieve the sample size using the following equation: X 17

18 The number of village required was randomly selected with ENA for SMART software Overall Coverage Estimate The point or period coverage estimate was chosen for SAM coverage. By method of Bayesian betabinomial conjugate analysis the prior probability density was combined with the coverage estimate from the likelihood survey to calculate the mode of posterior probability density. The Posterior Probability is the estimate of the overall coverage: it represents the synthesis of the prior probability and likelihood generated by the calculator with Bayes credible interval (CI) of 95%. Recommendations and Action Plan: A final important step is the development of an action plan that clearly identifies the actions to be undertaken, indicators, evaluation methods and deadlines ORGANIZATION OF THE EVALUATION CMN technical support The team conducting the SQUEAC in Mandera West was made up of participants from SCI (Nairobi and Mandera), MOH and some enumerators from the community. This was done with remote technical support of the Coverage Monitoring Network (CMN). The CMN Project is a joint initiative by ACF, Save the Children, International Committee, Concern Worldwide, Helen Keller International and Valid International. The programme is funded by ECHO and USAID. This project aims to increase and improve coverage monitoring of the Community Management of Acute malnutrition (CMAM) programme globally and build capacities of national and international nutrition professionals; in particular across the West, Central, East & Southern African countries where the CMAM approach is used to treat acute malnutrition. It also aims to identify, analyse and share lessons learned to improve the IMAM policy and practice across the areas with a high prevalence of acute malnutrition. The remote technical and methodological support was provided by a Regional Coverage Advisor (RECO) Inés ZUZA SANTACILIA through . SQUEAC plan in Annex Team training, logistic organization and evaluation development The investigation team (described in Annex 3) was composed of members of SCI from Wajir, and one person from Nairobi, MoH staff (1 DNOS ) and enumerators from the community. The SQUEAC was coordinated in the field by SCI Nutrition M&E Specialist (Caroline Njeri KIMERE) with support from the nutrition coordinator (Caroline KAWIRA), Nutrition Officer (Dorcas WANJIRU, Sub County Nutrition Officer (Patrick KAMUNDI with remote support from CMN RECO (Inés ZUZA SANTACILIA) through s. 7 Available at: 18

19 A two days training in the SQUEAC methodology was made to the enumerators by the team in Elwak town followed by the actual survey. For the three steps the investigation team was divided in three teams, composed by normally three people each LIMITATIONS The evaluation was limited by the following elements: - Poor data recording in the OTP registers which gave wrong performance indicators - Some of the survey personnel were subjective as opposed to being objective in collecting information from the interviewees so that they sometimes provided their own interpretation into the answers provided by interview respondents - Time limitation for training enumerators on interviewing skills and documentation of barriers and boosters - Some beneficiaries were shying from giving negative information to the team who were in the company of MoH and save the Children staff - Barriers were one sided as most team were led by MoH staff who felt that the implementation of the OTP program is the responsibility of Save the Children with support from MoH - Some beneficiaries were not open enough as regards to utilization of plumpynut at home they shied revealing there was sharing at household level leading to long length of stay. 19

20 4. RESULTS 4.1. STAGE Recommendations follow up of SQUEAC December 2011 Table 2: Evaluation of uptake of recommendations of the SQUEAC of Aug-Sept 2012 and the follow up, Mandera West, Kenya. 0ctober 2013 Achieved Not Recommendations Fully Partially Achieve d On-going 1. Community mobilisation a. Enhance efforts to include as many key field x sources of referral b. Include the MTMSGs in the mobilisation x strategy c. Work with local authorities to follow up on x negligent caretakers d. MoH to address allegations of favouritism x 2. Community sensitization: There is need for continued awareness targeting the entire community as regards a. Malnutrition; causes and signs and myths x related to program activities b. Sale and use of plumpy nut from the markets x 3. Program a. Ensure beneficiaries are admitted to respective x programs as per admission cut-offs. b. Contingency planning during periods such as x when program is inaccessible c. Continued implementation of program in x outreach sites. 4. Monitoring and evaluation a. Monitor migratory patterns to not only link x beneficiaries to proximal sites to new settlements but also ensure there are no double registrations in different sites. b. Constantly monitor program trends to ensure x the program is responsive to the context 20

21 particularly in Mandera Central c. Compute all program indicators to enhance monitoring i.e. include average length of stay, average weight gain and non-response rates. d. Map out all villages in the site areas and indicate the village of origin on the admission cards to allow for assessment of admissions per village in addition to the sites. e. Indicate the source of referral on admission cards to assess the effectiveness of referral sources in the community. 5. Advocacy a. MoH to continue advocating for more staffing particularly for health facilities that do not have nurses. b. Save the children and partner NGOs to explore supporting MoH with staff c. Continued advocacy for increment of CHW payment. x x x x x x 6. Survey preparation a. Timing of survey: the survey should be conducted when the program is fully operational. b. Provision and preparation of all required data in a timely manner c. Plan for sufficient time to collect all the required data adequately. 7. Seek to address on time the factors with potential to be barriers to coverage namely: a. Distance to access sites for a proportion of the community b. Consistent provision of drugs by the outreach teams or timely communication to community in case of shortages. c. In the event of contaminated consignments, timely and adequate re-assurance to the community that the program. x x x x x x 21

22 Quantitative data analysis a. Needs response : admissions and defaulters trends compared to seasonal and key events calendar Figure number 2 shows the OTP admission over a 20 -month period (January 2012 August 2013). This graph is aligned with seasonal and Key event calendar developed by the investigation team (weather patterns, seasonal calendar of human diseases associated with SAM in children, food availability, and workload). Together these two figures helped evaluate to what extent the program responds to seasonal needs. For the period under review, (Jan 2012 to August 2013), 2626 SAM children were admitted to OTPs and SC with an average of 131 children admitted per month. There were 113 defaulters along these months. Figure 3: OTP admission patterns over time compared with seasonal event calendar, Mandera West Sub- County, KENYA. Jan 2011-September 2013 Season floods Hunger gap Diarrhoea Respiratory infections Malaria Skin Disease Farming J F M A M J J A S O N D J F M A M J J A S Jilaal (dry) Gan (rainy) Adoles (dry) Haggay (rainy) Jilaal (dry) Gan (rainy) Adoles (dry) ramadhan idd BSFP Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Admissions Smooth Defaulters Smooth *idd : Islamic holidays The SAM admission trends are reflecting few months of the year trends. The hunger gap is from January to Mid-April with a peak March. There was a reduction in admissions in the 2012 since this 22

23 Aug Oct Dec Feb Apr Jun Aug Oct Dec Feb Apr Jun Aug Oct Dec Feb Apr Jun Aug Number of cases was recovery year after a severe drought in Blanket supplementation as well early in 2012 also may have contributed to improved nutrition status. There was a pick in admission in February-march 2013 which could be because an increase in infections (diarrhoea, respiratory infections) during the rainy seasons. b. OTP vs SC admissions The percentage of children admitted to the SC could be an indicator of the timeliness of admissions. It is directly related to the percentage of SAM cases that arrive at the OTP with associated medical complications. Children remaining untreated for long periods with declining nutritional status develop medical complications and end up needing SC care. A high percentage of SAM cases with medical complications could often be the product of a late presentation and uptake of services. In Mandera West district, the proportion of program admissions requiring inpatient care from January 2012 to August 2013 was 0.8%. This is not a true reflection because data from 15 of the 20 months. There were issues with data being uploaded in the District Health Information System (DHIS) which is an issue that has been flagged to the county health management team. Therefore the data should be interpreted with caution Figure 4: OTP admission compared with SC admissions. Mandera West, August 2010 to August Adm OTP Smooth ADM SC Smooth c. Admissions by OTP Figure 6 below shows the number of SAM cases admitted per OTP over a 13-month period (August 2012 August 2013). Dandu Health had most of the admissions (235). The OTP site with the least admissions was Guba which had only 34 admissions. 23

24 Figure 5: SAM admissions per OTP site. Mandera West Sub-County, KENYA. August 2012-August Figure 7 below shows the percentage of SAM cases admitted per OTP and the percentage of population of the catchment area per OTP over the 18-month period (January 2012 June 2013). Dandu, Burduras, Gither, and Nomadic health facilities received proportionally much more percentage of cases than expected for their catchment area compared to Takaba, Banisa, Kiliweheri and Guba. This could be attributed to catchment population calculations being done without taking into consideration admissions from nearest villages and as well double registration in the program therefore. Figure 6: Percentage of SAM admissions per OTP and Percentage of population catchment area. Mandera West Sub-County, Kenya. August 2012 August

25 d. Admissions MUAC Admission MUAC is an indicator for late /early presentation and service uptake at the OTP level. It can be a measure of direct coverage failure because late admissions are those non-covered SAM cases that went untreated for a significant period of time. Very late admissions almost always require inpatient care and are associated with prolonged treatment, defaulting and poor treatment outcomes. The Median admission MUAC for Mandera West was 113 however there were a couple of late admissions with 12.5% being below 10.5cm therefore at greater risk of death. The median MUAC at admission in general was very similar in all OTP ranging from 103mm and 114mm. The OTPs with the least median MUAC at admissions were Burashum and Dirbor recorded at 103mm while some like Funanteso, Tarama, Umur among others had a median MUAC of 114mm. During the analysis of MUAC data, it was noted that there was an over -representation of rounded values (i.e. 110mm, 105 mm, 100 mm, 90mm etc.) was observed, indicating imprecision in the MUAC measurement. Figure 7: MUAC at OTP admission. Mandera West Sub-County, KENYA. May-Sept 2013 e. Admission by type In the country, admissions for OTP are based on MUAC < 115 mm with (with length > 65 cm), and or WHZ score <-3 and or presence of bilateral pitting edema. In Mandera West 50.2% (308) of the OTP admissions were based on WHZ score while 39.4% (242) were based on MUAC as shown in the figure below. 25

26 Aug Oct Dec Feb Apr Jun Aug Oct Dec Feb Apr Jun Aug Oct Dec Feb Apr Jun Aug Percentage Number Figure 8: Number of admissions by the different admission criterions Mandera West March to August MUAC W/H Oedema MUAC + W/H Admission critaria f. Performance indicators The performance indicators for the Sub-County were within the acceptable SPHERE standards from August 2010 to August With the exception of 2010 the indicators were fairly stable in 2011 and 2012 but starting 2013 there was an increase in non-response and defaulting which could be attributed to rigorous on the job training (OJT) and data cleaning efforts by Save the child ren and MOH. Figure 9: Performance Indicators Mandera West August 2010 to August % 80% 60% 40% 20% 0% Cured Smooth Died Smooth Defaulted Smooth Non-response smooth g. Discharged cured The length of stay before recovery provides helpful insight into the duration of the treatment episode (e.g. the time from admission to discharge). In figure 12, the median length of stay for those discharged cured was 9 weeks some stayed for > 15 weeks in the program 26

27 No of Children No Of beneficiaries The international standards define typical LoS should be between days (4 to 6 weeks) to a maximum of 8 weeks. In this case the maximum length of stay was >15 weeks Figure 10: Length of stay for discharge cured. Mandera West Sub-County, KENYA. May-September >15 No of Weeks h. Defaulters Figure 13 shows the median length of stay before defaulting in Mandera West (May to Aug 2013). A Short length of stay before default can suggest a poor reception or communication between beneficiary and health staff. On the other hand defaulters after several weeks of treatment could be related to long length stays (caretaker assuming the children is cured or tired of keeping on the treatment). In Mandera West, The median length of stay for defaulters was 4 weeks. There was however a considerable number of children who defaulted at 1 st and 2 nd week sites that were affected by the clan clashes and therefore some beneficiaries moved to other sites in fear leading to defaulting Figure 11: OTP Length of stay before defaulting, Mandera West, KENYA >15 No of weeks 27

28 Qualitative data analysis The qualitative methods used included focus groups, semi-structured and structured interviews, cases studies and observations. Doing so revealed boosters and barriers. Interviews and focus groups were conducted in villages across the sub county. Questionnaire guides were adapted and oriented to facilitate the collection of data pertinent to program coverage and access. The investigation team also elaborated a list of terminology in the local languages (Annex 4) related to malnutrition and the RUTF. Qualitative data was triangulated by both method and source. All findings were indexed daily into the three-pane BBQ framework (complete BBQ can be found in Annex 5). Table 1 lists the sources and methods used during qualitative data collection. Questions ("Q") that appeared along stage one were analysed and resolved within days. Table 3:. SQUEAC BBQ framework legend. Mandera West Sub-County, KENYA. October 2013 Code Source Code Method 1. SAM caretakers 2. Local authorities (religious, chief villages/elders) 3. CHW/CHEW 4. OTP/ SC Nurse 5. Community of Women 6. Community of Men 7. SCI programme staff 8. County /sub county health authorities 9. Mother to mother support group (TBA) 10. Traditional healers/traditional dentist, TBA A. Group Discussion B. Semi Structured Interview C. Case Study D. Observation E. Data Analysis F. Last SQUEAC Table 2 details the principal factors that either negatively or positively influenced program coverage and access during the qualitative data analysis in Mandera West; these are the main barriers and boosters. Table 4: Main program barriers and boosters after qualitative data analysis. Mandera West Sub-county, Kenya. October 2013 Boosters Availability of CHWs during distribution creating trust & reducing waiting time Availability of free services for children under the age of five years & integration of services at health facilities and outreaches Barriers Lack of awareness of malnutrition by community members and some caretakers Inadequate mobilization and poor active case finding by the community health workers Appreciation of the program by Too little time spent with beneficiaries by 28

29 caretakers and community members occasioned by good treatment outcomes and also ambulatory services for children with SAM with complications from the villages to the stabilization centre. Proximity to OTP sites because of the outreach strategy Continuous RUTF stock availability at district level the OTP nurses and CHWs during distribution days Migration and nomadic nature of some part of the population. Poor human resources (too few nurses and CHWs and frequent turn over) 4.2. STAGE 2 This stage confirms the location of areas of high and low coverage and the reasons for coverage failure identified in Stage 1 using small studies, small surveys or small-area surveys. The routine program, quantitative and qualitative data collected in stage one, when combined, helped identify areas within the intervention zone where coverage was likely to be either satisf actory or unsatisfactory. This information was used to formulate hypotheses about coverage that were tested. Small-area surveys methodology were used to test this hypotheses. Areas with high coverage were agreed to be areas with an active CHW, where distance to the OTP site was near, and where there was good security while low coverage would be the opposite of that. Table 5: Small-area Survey selected villages for, Mandera West Sub-County, KENYA. October 2013 Low coverage SAM areas Outreach site / HF CHW Insecurity Kukub Yes Yes No Lulis Yes Yes No Eymole HF Not so active No Derkale HF Yes Yes Tarama Yes Yes Yes Kotkot Yes Yes No Didkuro No NO No High coverage SAM areas Outreach site / HF CHW Insecurity Gagaba HF Very active No Gither HF Very active No Tesoramu Outr Very active No Funanteso Outr Very active No Kudihalo Outr Yes No 29

30 The LQAS classification technique was used to analyze the data. The threshold value «p» used was 50%, and the results have been the following. - Low coverage: n=9 (nine SAM cases were found); four out of these cases were covered in OTP. d = (9 x (50/100) =4.5 ~ 4 Since 4 = 4». Since the covered cases are not greater than 4, the coverage in the surveyed area is classified as being below 50%. There was confirmation of hypothesis of low coverage area. - High coverage: n=2 (two SAM cases were found); both the cases were in OTP. d = (2 x (50/100) =1. Since 2 is >1» the hypothesis of high coverage area was confirmed. Areas with active CHW, near to the OTP site and with good security were confirmed to be high coverage areas. The hypothesis of low coverage area was also confirmed: areas with no CHW or not very active, distant to the OTP site and with insecurity (some of the villages). 30

31 4.3. STAGE 3 2 The prior As explained in the methods, the prior mode for the SAM program was calculated using the mean of the three coverage estimates: 1. The simple BBQ; 2. The weighted BBQ; and 3. The concept map. Table 6 details the calculation of the prior mode. Table 6: SAM program prior probability mode calculation, Mandera West, KENYA. October2013 Boosters Barriers Results (in %) Simple BBQ* ((22*5))+ (100-(28*5)))/ Weight BBQ (76+ (100-68))/ Concept Map (41+ (100 39))/ Histogram 38.0 Averaged prior 44.5 Next, using the equations presented in methodology 3, the shape parameters and were calculated with a prior mode of 44.5% about which the range of uncertainty was 19.5% and 69.5% (+/-25%). was 15.4 and was The distribution of the prior probability density has a mode at 44.5% and a 95% credible interval (i.e. the Bayesian equivalent of the 95% confidence interval as shown in figure 14 below. Figure 12: SAM prior coverage (binomial probability density), Mandera West, KENYA October